{"id":"W2903779322","doi":"10.21606/drs.2018.578","title":"Using Design Competencies to Define Curricula and Support Learners","year":2018,"lang":"en","type":"article","venue":"Proceedings of DRS","topic":"Design Education and Practice","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario College of Art and Design; Beef Farmers of Ontario","funders":"","keywords":"Curriculum; Computer science; Set (abstract data type); Core competency; Design elements and principles; Design education; Visualization; Mathematics education; Knowledge management; Software engineering; Psychology; Pedagogy; Artificial intelligence; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002040874,0.00007820965,0.00009655324,0.0000908431,0.00004071966,0.00003622423,0.00006832268,0.00002885475,0.00005969564],"category_scores_gemma":[0.000113339,0.00007908828,0.00001222715,0.0002074528,0.0000475487,0.0002249618,0.00001685533,0.00005495834,0.0000291855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001767641,"about_ca_system_score_gemma":0.00001380406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005880359,"about_ca_topic_score_gemma":2.705727e-7,"domain_scores_codex":[0.9995573,0.000001985087,0.0001174297,0.00009595863,0.0000926834,0.0001346272],"domain_scores_gemma":[0.9996871,0.00003084478,0.0000322813,0.00002966423,0.0001394389,0.00008063579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006142222,0.00007994899,0.01399483,0.000360985,0.00007743773,0.000001050293,0.01307169,0.000727242,0.9104608,0.007246724,0.03301616,0.02090173],"study_design_scores_gemma":[0.001334284,0.001480791,0.01569029,0.0004760108,0.0003186393,0.0003334522,0.01811528,0.0532135,0.7307647,0.001798438,0.1746565,0.001818078],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622609,0.00008728162,0.005820808,0.0001901038,0.0002276418,0.0001754515,0.000001123088,0.0001375555,0.03109915],"genre_scores_gemma":[0.9474449,0.00001862291,0.05224304,0.00009009253,0.00006785003,0.000004024736,2.852867e-7,0.00001551729,0.0001156569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.179696,"threshold_uncertainty_score":0.3225127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05821866781570666,"score_gpt":0.291650496428274,"score_spread":0.2334318286125673,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}